In modern high-volume customer engagement centers (CEC), there are a number of ways communication between a customer service representative (CSR) and a customer can take place. For example, communication in a CEC can take place over the telephone, through email, and text chat, among other known communication methods. Further it is often the case that a customer contact or communication requires a wide variety of communication protocols and resources. It is extremely important for a CEC system to provide efficient and proper routing of incoming customer communication. It is just as important for a CEC system to provide CSRs with accurate and helpful guidance for servicing the incoming customer communications routed to them.
There are a number of methods and systems designed to assist in the routing of incoming customer communications and provide recommendations to CSRs to assist in servicing the incoming customer communication. Typically, these methods and systems receive initial data regarding the incoming communication and determine how to route/provide recommendations based on that initial data and a set of static generalized rules. Further, these known systems and methods do not utilize the different types of feedback a company may receive from when making a routing/recommendation determination.
For example, the system may have static rules that indicate certain types of communications, such as phone calls about changing services, are to be directed to one of particular group of CSRs. Those CSRs might also be assigned to handle other types of interactions as well. When an incoming communication occurs, the system will route all phone calls about changing services to that set of CSRs. The system might also even have a general ranking of each CSR and take the general ranking into account when routing or determining guidance to recommend. However, typical systems are not robust enough to determine that one of the CSRs assigned to the group for handling phone calls about changing services has been getting poor feedback on those particular types of calls. Further, due to the nature of static rules that are manually updated when changes are needed, even if the system is robust enough, the rule change would need to be implemented manually.
In another example, a CSR assigned to communicate with a high-value customer may mistakenly use an old communication protocol still stored in the CEC computer system as opposed to a new protocol designed to account for the customer's new preferences. By the time the CSR realizes their mistake, the customer relationship may be damaged.
There is an unmet need in the art for a system capable of automatically analyzing customer service interactions including feedback provided by customer surveys, feedback provided by customer sentiment, and feedback provided by the results of decisions made by CSRs and automatedly updating routing rules and recommendation rules based on the analysis.